



import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A__								=		imread(r'''digital_image_processing/image_data/lena_small.jpg''')
A__								=		cvtColor(img, COLOR_BGR2GRAY)



def      _lowerCAmelCase			(   )  ->   Optional[int]:


				"""simple docstring"""

				snake_case__      : Dict							=      cn.convert_to_negative(UpperCamelCase__ )
				# assert negative_img array for at least one True
				assert negative_img.any()



def      _lowerCAmelCase			(   )  ->   Any:


				"""simple docstring"""

				with Image.open('''digital_image_processing/image_data/lena_small.jpg''' ) as img:
								# Work around assertion for response
								assert str(cc.change_contrast(UpperCamelCase__    ,    110 ) ).startswith(
								    '''<PIL.Image.Image image mode=RGB size=100x100 at''' )



def      _lowerCAmelCase			(   )  ->   Optional[int]:


				"""simple docstring"""

				snake_case__      : int							=      canny.gen_gaussian_kernel(9    ,    sigma=1.4 )
				# Assert ambiguous array
				assert resp.all()



def      _lowerCAmelCase			(   )  ->   int:


				"""simple docstring"""

				snake_case__      : Optional[int]							=      imread('''digital_image_processing/image_data/lena_small.jpg'''    ,    0 )
				# assert ambiguous array for all == True
				assert canny_img.all()
				snake_case__      : Any							=      canny.canny(UpperCamelCase__ )
				# assert canny array for at least one True
				assert canny_array.any()



def      _lowerCAmelCase			(   )  ->   Union[str, Any]:


				"""simple docstring"""

				assert gg.gaussian_filter(UpperCamelCase__    ,    5    ,    sigma=0.9 ).all()



def      _lowerCAmelCase			(   )  ->   Dict:


				"""simple docstring"""

				snake_case__      : Optional[int]							=      array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
				snake_case__      : Union[str, Any]							=      conv.img_convolve(UpperCamelCase__    ,    UpperCamelCase__ ).astype(UpperCamelCase__ )
				assert res.any()



def      _lowerCAmelCase			(   )  ->   Optional[Any]:


				"""simple docstring"""

				assert med.median_filter(UpperCamelCase__    ,    3 ).any()



def      _lowerCAmelCase			(   )  ->   Optional[int]:


				"""simple docstring"""

				snake_case__						,					snake_case__      : int							=      sob.sobel_filter(UpperCamelCase__ )
				assert grad.any() and theta.any()



def      _lowerCAmelCase			(   )  ->   Any:


				"""simple docstring"""

				snake_case__      : List[Any]							=      sp.make_sepia(UpperCamelCase__    ,    20 )
				assert sepia.all()



def      _lowerCAmelCase			(   __lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg" )  ->   Tuple:


				"""simple docstring"""

				snake_case__      : int							=      bs.Burkes(imread(UpperCamelCase__    ,    1 )    ,    120 )
				burkes.process()
				assert burkes.output_img.any()



def      _lowerCAmelCase			(   __lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"    ,    )  ->   List[Any]:


				"""simple docstring"""

				snake_case__      : Optional[Any]							=      rs.NearestNeighbour(imread(UpperCamelCase__    ,    1 )    ,    400    ,    200 )
				nn.process()
				assert nn.output.any()





def      _lowerCAmelCase			(   )  ->   int:


				"""simple docstring"""

				snake_case__      : str							=      '''digital_image_processing/image_data/lena.jpg'''

				# Reading the image and converting it to grayscale.
				snake_case__      : Union[str, Any]							=      imread(UpperCamelCase__    ,    0 )

				# Test for get_neighbors_pixel function() return not None
				snake_case__      : Any							=      0
				snake_case__      : int							=      0
				snake_case__      : Union[str, Any]							=      image[x_coordinate][y_coordinate]

				snake_case__      : Union[str, Any]							=      lbp.get_neighbors_pixel(
				    UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__ )

				assert neighbors_pixels is not None

				# Test for local_binary_pattern function()
				# Create a numpy array as the same height and width of read image
				snake_case__      : Union[str, Any]							=      np.zeros((image.shape[0], image.shape[1]) )

				# Iterating through the image and calculating the local binary pattern value
				# for each pixel.
				for i in range(0    ,    image.shape[0] ):
								for j in range(0    ,    image.shape[1] ):
												snake_case__      : Tuple							=      lbp.local_binary_value(UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__ )

				assert lbp_image.any()


'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       float:
    if digit_amount > 0:
        return round(number - int(UpperCamelCase__							)							,    UpperCamelCase__							)
    return number - int(UpperCamelCase__							)


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))
